Retinal Optical Coherence Tomography Image Denoising Using Modified Soft Thresholding Wavelet Transform
Bibliographic record
Abstract
Optical Coherence Tomography (OCT) represents a non-invasive imaging modality capable of capturing high-resolution cross-sectional images of anatomical structures by scanning the tissue of interest in a transverse manner.Nevertheless, the inherent speckle noise present in OCT images considerably degrades their textural and sharpness qualities.Conventional wavelet-based modified soft thresholding methods have been employed to preserve pertinent information in denoising OCT images, but their performance remains contingent upon hyperparameter tuning.In this study, we introduce a Particle Swarm Optimization (PSO)-based optimized Wavelet Threshold (WT) method for OCT image denoising.By automating the process of determining hyperparameter values dependent on image quality, PSO streamlines the denoising process.The optimization problem's fitness function is defined by the Peak Signal-to-Noise Ratio (PSNR) parameter.To evaluate the WT-PSO algorithm, we utilized performance metrics such as Mean Square Error (MSE), PSNR, Structural Similarity Index Metrics (SSIM), and Contrast-to-Noise Ratio (CNR) on a publicly available dataset comprising 17 retinal OCT images.The proposed denoising approach demonstrates comparable results to those obtained by manual iterative or trial methods, delivering marginal improvements in performance parameters and image quality.Moreover, our method outperforms traditional wavelet-based state-of-the-art techniques for denoising OCT images, highlighting its potential for widespread application in the field.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".